目標:
求出Titanic女乘客的生還率

import seaborn as sns
import numpy as np
import pandas as pd

df = sns.load_dataset('titanic')
print(df.info())

執行結果：
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 15 columns):
 #   Column       Non-Null Count  Dtype   
---  ------       --------------  -----   
 0   survived     891 non-null    int64   
 1   pclass       891 non-null    int64   
 2   sex          891 non-null    object  
 3   age          714 non-null    float64 
 4   sibsp        891 non-null    int64   
 5   parch        891 non-null    int64   
 6   fare         891 non-null    float64 
 7   embarked     889 non-null    object  
 8   class        891 non-null    category
 9   who          891 non-null    object  
 10  adult_male   891 non-null    bool    
 11  deck         203 non-null    category
 12  embark_town  889 non-null    object  
 13  alive        891 non-null    object  
 14  alone        891 non-null    bool    
dtypes: bool(2), category(2), float64(2), int64(4), object(5)
memory usage: 80.7+ KB
None

前10筆

print(df.head(10))

執行結果：
     who  adult_male deck  embark_town alive  alone  
0    man        True  NaN  Southampton    no  False  
1  woman       False    C    Cherbourg   yes  False  
2  woman       False  NaN  Southampton   yes   True  
3  woman       False    C  Southampton   yes  False  
4    man        True  NaN  Southampton    no   True  
5    man        True  NaN   Queenstown    no   True  
6    man        True    E  Southampton    no   True  
7  child       False  NaN  Southampton    no  False  
8  woman       False  NaN  Southampton   yes  False  
9  child       False  NaN    Cherbourg   yes  False

查看空查狀況

print(df.isnull().sum())

執行結果：
survived         0
pclass           0
sex              0
age            177
sibsp            0
parch            0
fare             0
embarked         2
class            0
who              0
adult_male       0
deck           688
embark_town      2
alive            0
alone            0
dtype: int64

將「age」欄位缺值，使用中位數補值

df.loc[:, 'age'] = df.loc[:, 'age'].fillna(df.loc[:, 'age'].median())
print(df.info())

執行結果：
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 15 columns):
 #   Column       Non-Null Count  Dtype   
---  ------       --------------  -----   
 0   survived     891 non-null    int64   
 1   pclass       891 non-null    int64   
 2   sex          891 non-null    object  
 3   age          891 non-null    float64 
 4   sibsp        891 non-null    int64   
 5   parch        891 non-null    int64   
 6   fare         891 non-null    float64 
 7   embarked     889 non-null    object  
 8   class        891 non-null    category
 9   who          891 non-null    object  
 10  adult_male   891 non-null    bool    
 11  deck         203 non-null    category
 12  embark_town  889 non-null    object  
 13  alive        891 non-null    object  
 14  alone        891 non-null    bool    
dtypes: bool(2), category(2), float64(2), int64(4), object(5)
memory usage: 80.7+ KB
None

將「embarked」欄位缺值，使用眾數補值

mod = df.loc[:,'embark_town'].mode()
print(mod)
df.loc[:,'embark_town'].fillna(mod[0], inplace=True)
print(df.info())

執行結果：
0    Southampton
Name: embark_town, dtype: object
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 15 columns):
 #   Column       Non-Null Count  Dtype   
---  ------       --------------  -----   
 0   survived     891 non-null    int64   
 1   pclass       891 non-null    int64   
 2   sex          891 non-null    object  
 3   age          891 non-null    float64 
 4   sibsp        891 non-null    int64   
 5   parch        891 non-null    int64   
 6   fare         891 non-null    float64 
 7   embarked     891 non-null    object  
 8   class        891 non-null    category
 9   who          891 non-null    object  
 10  adult_male   891 non-null    bool    
 11  deck         203 non-null    category
 12  embark_town  891 non-null    object  
 13  alive        891 non-null    object  
 14  alone        891 non-null    bool    
dtypes: bool(2), category(2), float64(2), int64(4), object(5)
memory usage: 80.7+ KB
None

將「deck」欄位缺值過多，將此欄位刪除

df = df.drop(['deck'],axis=1)
print(df.info())

執行結果：
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 14 columns):
 #   Column       Non-Null Count  Dtype   
---  ------       --------------  -----   
 0   survived     891 non-null    int64   
 1   pclass       891 non-null    int64   
 2   sex          891 non-null    object  
 3   age          891 non-null    float64 
 4   sibsp        891 non-null    int64   
 5   parch        891 non-null    int64   
 6   fare         891 non-null    float64 
 7   embarked     891 non-null    object  
 8   class        891 non-null    category
 9   who          891 non-null    object  
 10  adult_male   891 non-null    bool    
 11  embark_town  891 non-null    object  
 12  alive        891 non-null    object  
 13  alone        891 non-null    bool    
dtypes: bool(2), category(1), float64(2), int64(4), object(5)
memory usage: 79.4+ KB
None

求女性生還率多有多高

df = df.query('sex == "female"')
survived_rate = df['survived'].mean()
print(survived_rate)

執行結果：
0.7420382165605095

另一種算法

df = df.query('sex == "female"')
survived_rate = pd.crosstab(df['survived'], df['sex']).iloc[1, 0] / df.shape[0]
print(survived_rate)

執行結果：
0.7420382165605095

Titanic女乘客的生還率為 74.26%。